Spectral Reflectance Recovery From a Single RGB Image

Fu, Ying; Zheng, Yongrong; Zhang, Lin; Huang, Hua

doi:10.1109/tci.2018.2855445

Cited by 56 publications

(36 citation statements)

References 43 publications

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“…The surface reflectance is denoted by ( , , ) at the location ( , ), and the sensitivity function of the camera for the th band is indicated by ( ). Then, the image value can be defined as follows [10]:…”

Section: Analysis Of Illumination Effectsmentioning

confidence: 99%

Enabling hyperspectral imaging in diverse illumination conditions for indoor applications

Moghadam

Sharma

Hefeeda

2021

Proceedings of the 12th ACM Multimedia Systems Conference

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This paper enables hyperspectral imaging in indoor applications using cost effective LED and CFL sources by a data-driven supervised learning model. It integrates four loss functions in the neural network model to restore hyperspectral bands and ensure their spatial and spectral accuracy. It conducts an extensive experimental study and shows the proposed model outperforms the state-of-the-art using real hyperspectral datasets that authors have collected. The reviewers recommended the acceptance of this paper in MMSys'21 for the following reasons. Public review written byTed (Zixia) Huang Google, USA

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Section: Analysis Of Illumination Effectsmentioning

confidence: 99%

Enabling hyperspectral imaging in diverse illumination conditions for indoor applications

Moghadam

Sharma

Hefeeda

2021

Proceedings of the 12th ACM Multimedia Systems Conference

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“…Recently, other ways of reconstructing spectral images from raw response images (usually RGB images) have been explored, including sparse reconstruction and deep learning. Sparse reconstruction methods exploit the sparsity of spectra, recover spectra for every pixels using an learnt overcomplete dictionary [23,24,25]. Based on sparse reconstruction, some literature has proved that the reconstruction accuracy would benefit from combining the prior of local manifold structure [16,26].…”

Section: Related Workmentioning

confidence: 99%

Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Xie

et al. 2019

Sensors

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Multispectral filter array (MSFA)-based imaging is a compact, practical technique for snapshot spectral image capturing and reconstruction. The imaging and reconstruction quality is highly influenced by the spectral sensitivities and spatial arrangement of channels on MSFAs, and the used reconstruction method. In order to design a MSFA with high imaging capacity, we propose a sparse representation based approach to optimize spectral sensitivities and spatial arrangement of MSFAs. The proposed approach first overall models the various errors associated with spectral reconstruction, and then uses a global heuristic searching method to optimize MSFAs via minimizing the estimated error of MSFAs. Our MSFA optimization method can select filters from off-the-shelf candidate filter sets while assigning the selected filters to the designed MSFA. Experimental results on three datasets show that the proposed method is more efficient, flexible, and can design MSFAs with lower spectral construction errors when compared with existing state-of-the-art methods. The MSFAs designed by our method show better performance than others even using different spectral reconstruction methods.

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“…T HE observed color of an object in a scene depends on its spectral reflectance and spectral composition of the illuminant. As a result, when the scene illuminant changes, the light reflected from the object also changes [1], [2]. The ability to filter out the color of the light source is called color constancy [1], [3].…”

Section: Introductionmentioning

confidence: 99%

“…It results in better quality images. For a robust color-based system, the illumination effects of the light source need to be discounted, so that colors present in the image reflect the intrinsic properties of the objects in the scene [2]- [5]. This is important for many high level image or video applications.…”

Section: Introductionmentioning

confidence: 99%

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INTEL-TAU: A Color Constancy Dataset

Laakom

Raitoharju

Nikkanen³

et al. 2021

IEEE Access

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In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.

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Spectral Reflectance Recovery From a Single RGB Image

Cited by 56 publications

References 43 publications

Enabling hyperspectral imaging in diverse illumination conditions for indoor applications

Enabling hyperspectral imaging in diverse illumination conditions for indoor applications

Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

INTEL-TAU: A Color Constancy Dataset

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